Facial Recognition with Social Network Aiding

ABSTRACT

A facial recognition search system identifies one or more likely names (or other personal identifiers) corresponding to the facial image(s) in a query as follows. After receiving the visual query with one or more facial images, the system identifies images that potentially match the respective facial image in accordance with visual similarity criteria. Then one or more persons associated with the potential images are identified. For each identified person, person-specific data comprising metrics of social connectivity to the requester are retrieved from a plurality of applications such as communications applications, social networking applications, calendar applications, and collaborative applications. An ordered list of persons is then generated by ranking the identified persons in accordance with at least metrics of visual similarity between the respective facial image and the potential image matches and with the social connection metrics. Finally, at least one person identifier from the list is sent to the requester.

RELATED APPLICATIONS

This application claims priority to the following U.S. ProvisionalPatent Applications which are incorporated by reference herein in theirentirety: U.S. Provisional Patent Application No. 61/232,397, filed Aug.7, 2009, entitled “Architecture for Responding to a Visual Query” andU.S. Provisional Patent Application No. 61/370,784, filed Aug. 4, 2010,entitled “Facial Recognition with Social Network Aiding.”

This application is related to the following U.S. Provisional PatentApplications all of which are incorporated by reference herein in theirentirety: U.S. Provisional Patent Application No. 61/266,116, filed Dec.2, 2009 entitled “Architecture for Responding to a Visual Query;” U.S.Provisional Patent Application No. 61/266,122, filed Dec. 2, 2009,entitled “User Interface for Presenting Search Results for MultipleRegions of a Visual Query;” U.S. Provisional Patent Application No.61/266,125, filed Dec. 2, 2009, entitled “Identifying Matching CanonicalDocuments In Response To A Visual Query;” U.S. Provisional PatentApplication No. 61/266,126, filed Dec. 2, 2009, entitled “Region ofInterest Selector for Visual Queries;” U.S. Provisional PatentApplication No. 61/266,130, filed Dec. 2, 2009, entitled “ActionableSearch Results for Visual Queries;” U.S. Provisional Patent ApplicationNo. 61/266,133, filed Dec. 2, 2009, entitled “Actionable Search Resultsfor Street View Visual Queries,” and U.S. Provisional Patent ApplicationNo. 61/266,499, filed Dec. 3, 2009, entitled “Hybrid Use Location SensorData and Visual Query to Return Local Listing for Visual Query.”

TECHNICAL FIELD

The disclosed embodiments relate generally to identifying one or morepersons who potentially match a face in an image query. by utilizingsocial network information and information obtained from other picturesof the identified person(s) to facilitate identification of the bestmatching person(s).

BACKGROUND

Text-based or term-based searching, wherein a user inputs a word orphrase into a search engine and receives a variety of results is auseful tool for searching. However, term based queries require that auser be able to input a relevant term. Sometimes a user may wish to knowinformation about an image. For example, a user might want to know thename of a person in a photograph. A person may also wish to know otherinformation, such as contact information, for a person in a photograph.Accordingly, a system that can receive a facial image query and providea variety of search results related to an identified person in thefacial image query would be desirable.

SUMMARY

According to some embodiments, a computer-implemented method ofprocessing a visual query including a facial image is performed on aserver system having one or more processors and memory storing one ormore programs for execution by the one or more processors. The methodincludes the process outlined below. A visual query comprising one ormore facial images including a respective facial image is received froma requester. Potential image matches that potentially match therespective facial image are identified in accordance with visualsimilarity criteria. The potential image matches comprise images fromone or more image sources identified in accordance with data regardingthe requester. One or more persons associated with the potential imagematches are identified. For each identified person, person-specific datacomprising social connection metrics of social connectivity to therequester obtained from a plurality of applications are retrieved. Theplurality of applications is selected from the group consisting ofcommunication applications, social networking applications, calendarapplications, and collaborative applications. An ordered list of personsis generated by ranking the one or more identified persons in accordancewith one or more metrics of visual similarity between the respectivefacial image and the potential image matches and also in accordance withranking information comprising at least the social connection metrics.Then at least one person identifier from the ordered list is sent to therequester. Such a method may also include program instructions toexecute the additional options discussed in the following sections.

According to some embodiments a server system is provided for processinga visual query including a facial image. The server system includes oneor more processors for executing programs and memory storing one or moreprograms be executed by the one or more processors. The one or moreprograms include instructions for the process as outlined below. Avisual query comprising one or more facial images including a respectivefacial image is received from a requester. Potential image matches thatpotentially match the respective facial image are identified inaccordance with visual similarity criteria. The potential image matchescomprise images from one or more image sources identified in accordancewith data regarding the requester. One or more persons associated withthe potential image matches are identified. For each identified person,person-specific data comprising social connection metrics of socialconnectivity to the requester obtained from a plurality of applicationsare retrieved. The plurality of applications is selected from the groupconsisting of communication applications, social networkingapplications, calendar applications, and collaborative applications. Anordered list of persons is generated by ranking the one or moreidentified persons in accordance with one or more metrics of visualsimilarity between the respective facial image and the potential imagematches and also in accordance with ranking information comprising atleast the social connection metrics. Then at least one person identifierfrom the ordered list is sent to the requester. Such a system may alsoinclude program instructions to execute the additional options discussedin the following sections.

According to some embodiments, a non-transitory computer readablestorage medium for processing a visual query including a facial image isprovided. The computer readable storage medium stores one or moreprograms configured for execution by a computer, the one or moreprograms comprising instructions for performing the following. A visualquery comprising one or more facial images including a respective facialimage is received from a requester. Potential image matches thatpotentially match the respective facial image are identified inaccordance with visual similarity criteria. The potential image matchescomprise images from one or more image sources identified in accordancewith data regarding the requester. One or more persons associated withthe potential image matches are identified. For each identified person,person-specific data comprising social connection metrics of socialconnectivity to the requester obtained from a plurality of applicationsare retrieved. The plurality of applications is selected from the groupconsisting of communication applications, social networkingapplications, calendar applications, and collaborative applications. Anordered list of persons is generated by ranking the one or moreidentified persons in accordance with one or more metrics of visualsimilarity between the respective facial image and the potential imagematches and also in accordance with ranking information comprising atleast the social connection metrics. Then at least one person identifierfrom the ordered list is sent to the requester. Such a computer readablestorage medium may also include program instructions to execute theadditional options discussed in the following sections.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a computer network that includesa visual query server system.

FIG. 2 is a flow diagram illustrating the process for responding to avisual query, in accordance with some embodiments.

FIG. 3 is a flow diagram illustrating the process for responding to avisual query with an interactive results document, in accordance withsome embodiments.

FIG. 4 is a flow diagram illustrating the communications between aclient and a visual query server system, in accordance with someembodiments.

FIG. 5 is a block diagram illustrating a client system, in accordancewith some embodiments.

FIG. 6 is a block diagram illustrating a front end visual queryprocessing server system, in accordance with some embodiments.

FIG. 7 is a block diagram illustrating a generic one of the parallelsearch systems utilized to process a visual query, in accordance withsome embodiments.

FIG. 8 is a block diagram illustrating an OCR search system utilized toprocess a visual query, in accordance with some embodiments.

FIG. 9 is a block diagram illustrating a facial recognition searchsystem utilized to process a visual query, in accordance with someembodiments.

FIG. 10 is a block diagram illustrating an image to terms search systemutilized to process a visual query, in accordance with some embodiments.

FIG. 11 illustrates a client system with a screen shot of an exemplaryvisual query, in accordance with some embodiments.

FIGS. 12A and 12B each illustrate a client system with a screen shot ofan interactive results document with bounding boxes, in accordance withsome embodiments.

FIG. 13 illustrates a client system with a screen shot of an interactiveresults document that is coded by type, in accordance with someembodiments.

FIG. 14 illustrates a client system with a screen shot of an interactiveresults document with labels, in accordance with some embodiments.

FIG. 15 illustrates a screen shot of an interactive results document andvisual query displayed concurrently with a results list, in accordancewith some embodiments.

FIGS. 16A-16B are flowcharts illustrating the process of responding to avisual query including a facial image, in accordance with someembodiments.

FIG. 17 is a flowchart illustrating various factors and characteristicsused in generating an ordered list of persons that potentially match afacial image in a visual query, in accordance with some embodiments.

FIGS. 18A is a block diagram illustrating a portion of the datastructure of a facial image database utilized by a facial recognitionsearch system, in accordance with some embodiments. FIG. 18B illustratesrelationships between people across a plurality of applications such associal network and communication applications, in accordance with someembodiments. FIG. 18C is a block diagram illustrating some image derivedcharacteristics, in accordance with some embodiments.

Like reference numerals refer to corresponding parts throughout thedrawings.

DESCRIPTION OF EMBODIMENTS

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present invention. However, it will beapparent to one of ordinary skill in the art that the present inventionmay be practiced without these specific details. In other instances,well-known methods, procedures, components, circuits, and networks havenot been described in detail so as not to unnecessarily obscure aspectsof the embodiments.

It will also be understood that, although the terms first, second, etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. For example, a first contact could be termed asecond contact, and, similarly, a second contact could be termed a firstcontact, without departing from the scope of the present invention. Thefirst contact and the second contact are both contacts, but they are notthe same contact.

The terminology used in the description of the invention herein is forthe purpose of describing particular embodiments only and is notintended to be limiting of the invention. As used in the description ofthe invention and the appended claims, the singular forms “a,” “an,” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. It will also be understood that theterm “and/or” as used herein refers to and encompasses any and allpossible combinations of one or more of the associated listed items. Itwill be further understood that the terms “comprises” and/or“comprising,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in response to detecting,” dependingon the context. Similarly, the phrase “if it is determined” or “if (astated condition or event) is detected” may be construed to mean “upondetermining” or “in response to determining” or “upon detecting (thestated condition or event)” or “in response to detecting (the statedcondition or event),” depending on the context.

FIG. 1 is a block diagram illustrating a computer network that includesa visual query server system according to some embodiments. The computernetwork 100 includes one or more client systems 102 and a visual queryserver system 106. One or more communications networks 104 interconnectthese components. The communications network 104 may be any of a varietyof networks, including local area networks (LAN), wide area networks(WAN), wireless networks, wireline networks, the Internet, or acombination of such networks.

The client system 102 includes a client application 108, which isexecuted by the client system, for receiving a visual query (e.g.,visual query 1102 of FIG. 11). A visual query is an image that issubmitted as a query to a search engine or search system. Examples ofvisual queries, without limitations include photographs, scanneddocuments and images, and drawings. In some embodiments, the clientapplication 108 is selected from the set consisting of a searchapplication, a search engine plug-in for a browser application, and asearch engine extension for a browser application. In some embodiments,the client application 108 is an “omnivorous” search box, which allows auser to drag and drop any format of image into the search box to be usedas the visual query.

A client system 102 sends queries to and receives data from the visualquery server system 106. The client system 102 may be any computer orother device that is capable of communicating with the visual queryserver system 106. Examples include, without limitation, desktop andnotebook computers, mainframe computers, server computers, mobiledevices such as mobile phones and personal digital assistants, networkterminals, and set-top boxes.

The visual query server system 106 includes a front end visual queryprocessing server 110. The front end server 110 receives a visual queryfrom the client 102, and sends the visual query to a plurality ofparallel search systems 112 for simultaneous processing. The searchsystems 112 each implement a distinct visual query search process andaccess their corresponding databases 114 as necessary to process thevisual query by their distinct search process. For example, a facerecognition search system 112-A will access a facial image database114-A to look for facial matches to the image query. As will beexplained in more detail with regard to FIG. 9, if the visual querycontains a face, the facial recognition search system 112-A will returnone or more search results (e.g., names, matching faces, etc.) from thefacial image database 114-A. In another example, the optical characterrecognition (OCR) search system 112-B, converts any recognizable text inthe visual query into text for return as one or more search results. Inthe optical character recognition (OCR) search system 112-B, an OCRdatabase 114-B may be accessed to recognize particular fonts or textpatterns as explained in more detail with regard to FIG. 8.

Any number of parallel search systems 112 may be used. Some examplesinclude a facial recognition search system 112-A, an OCR search system112-B, an image-to-terms search system 112-C (which may recognize anobject or an object category), a product recognition search system(which may be configured to recognize 2-D images such as book covers andCDs and may also be configured to recognized 3-D images such asfurniture), bar code recognition search system (which recognizes 1D and2D style bar codes), a named entity recognition search system, landmarkrecognition (which may configured to recognize particular famouslandmarks like the Eiffel Tower and may also be configured to recognizea corpus of specific images such as billboards), place recognition aidedby geo-location information provided by a GPS receiver in the clientsystem 102 or mobile phone network, a color recognition search system,and a similar image search system (which searches for and identifiesimages similar to a visual query). Further search systems can be addedas additional parallel search systems, represented in FIG. 1 by system112-N. All of the search systems, except the OCR search system, arecollectively defined herein as search systems performing an image-matchprocess. All of the search systems including the OCR search system arecollectively referred to as query-by-image search systems. In someembodiments, the visual query server system 106 includes a facialrecognition search system 112-A, an OCR search system 112-B, and atleast one other query-by-image search system 112.

The parallel search systems 112 each individually process the visualsearch query and return their results to the front end server system110. In some embodiments, the front end server 100 may perform one ormore analyses on the search results such as one or more of: aggregatingthe results into a compound document, choosing a subset of results todisplay, and ranking the results as will be explained in more detailwith regard to FIG. 6. The front end server 110 communicates the searchresults to the client system 102.

The client system 102 presents the one or more search results to theuser. The results may be presented on a display, by an audio speaker, orany other means used to communicate information to a user. The user mayinteract with the search results in a variety of ways. In someembodiments, the user's selections, annotations, and other interactionswith the search results are transmitted to the visual query serversystem 106 and recorded along with the visual query in a query andannotation database 116. Information in the query and annotationdatabase can be used to improve visual query results. In someembodiments, the information from the query and annotation database 116is periodically pushed to the parallel search systems 112, whichincorporate any relevant portions of the information into theirrespective individual databases 114.

The computer network 100 optionally includes a term query server system118, for performing searches in response to term queries. A term queryis a query containing one or more terms, as opposed to a visual querywhich contains an image. The term query server system 118 may be used togenerate search results that supplement information produced by thevarious search engines in the visual query server system 106. Theresults returned from the term query server system 118 may include anyformat. The term query server system 118 may include textual documents,images, video, etc. While term query server system 118 is shown as aseparate system in FIG. 1, optionally the visual query server system 106may include a term query server system 118.

Additional information about the operation of the visual query serversystem 106 is provided below with respect to the flowcharts in FIGS.2-4.

FIG. 2 is a flow diagram illustrating a visual query server systemmethod for responding to a visual query, according to certainembodiments of the invention. Each of the operations shown in FIG. 2 maycorrespond to instructions stored in a computer memory or computerreadable storage medium.

The visual query server system receives a visual query from a clientsystem (202). The client system, for example, may be a desktop computingdevice, a mobile device, or another similar device (204) as explainedwith reference to FIG. 1. An example visual query on an example clientsystem is shown in FIG. 11.

The visual query is an image document of any suitable format. Forexample, the visual query can be a photograph, a screen shot, a scannedimage, or a frame or a sequence of multiple frames of a video (206). Insome embodiments, the visual query is a drawing produced by a contentauthoring program (736, FIG. 5). As such, in some embodiments, the user“draws” the visual query, while in other embodiments the user scans orphotographs the visual query. Some visual queries are created using animage generation application such as Acrobat, a photograph editingprogram, a drawing program, or an image editing program. For example, avisual query could come from a user taking a photograph of his friend onhis mobile phone and then submitting the photograph as the visual queryto the server system. The visual query could also come from a userscanning a page of a magazine, or taking a screen shot of a webpage on adesktop computer and then submitting the scan or screen shot as thevisual query to the server system. In some embodiments, the visual queryis submitted to the server system 106 through a search engine extensionof a browser application, through a plug-in for a browser application,or by a search application executed by the client system 102. Visualqueries may also be submitted by other application programs (executed bya client system) that support or generate images which can betransmitted to a remotely located server by the client system.

The visual query can be a combination of text and non-text elements(208). For example, a query could be a scan of a magazine pagecontaining images and text, such as a person standing next to a roadsign. A visual query can include an image of a person's face, whethertaken by a camera embedded in the client system or a document scanned byor otherwise received by the client system. A visual query can also be ascan of a document containing only text. The visual query can also be animage of numerous distinct subjects, such as several birds in a forest,a person and an object (e.g., car, park bench, etc.), a person and ananimal (e.g., pet, farm animal, butterfly, etc.). Visual queries mayhave two or more distinct elements. For example, a visual query couldinclude a barcode and an image of a product or product name on a productpackage. For example, the visual query could be a picture of a bookcover that includes the title of the book, cover art, and a bar code. Insome instances, one visual query will produce two or more distinctsearch results corresponding to different portions of the visual query,as discussed in more detail below.

The server system processes the visual query as follows. The front endserver system sends the visual query to a plurality of parallel searchsystems for simultaneous processing (210). Each search system implementsa distinct visual query search process, i.e., an individual searchsystem processes the visual query by its own processing scheme.

In some embodiments, one of the search systems to which the visual queryis sent for processing is an optical character recognition (OCR) searchsystem. In some embodiments, one of the search systems to which thevisual query is sent for processing is a facial recognition searchsystem. In some embodiments, the plurality of search systems runningdistinct visual query search processes includes at least: opticalcharacter recognition (OCR), facial recognition, and anotherquery-by-image process other than OCR and facial recognition (212). Theother query-by-image process is selected from a set of processes thatincludes but is not limited to product recognition, bar coderecognition, object-or-object-category recognition, named entityrecognition, and color recognition (212).

In some embodiments, named entity recognition occurs as a post processof the OCR search system, wherein the text result of the OCR is analyzedfor famous people, locations, objects and the like, and then the termsidentified as being named entities are searched in the term query serversystem (118, FIG. 1). In other embodiments, images of famous landmarks,logos, people, album covers, trademarks, etc. are recognized by animage-to-terms search system. In other embodiments, a distinct namedentity query-by-image process separate from the image-to-terms searchsystem is utilized. The object-or-object category recognition systemrecognizes generic result types like “car.” In some embodiments, thissystem also recognizes product brands, particular product models, andthe like, and provides more specific descriptions, like “Porsche.” Someof the search systems could be special user specific search systems. Forexample, particular versions of color recognition and facial recognitioncould be a special search systems used by the blind.

The front end server system receives results from the parallel searchsystems (214). In some embodiments, the results are accompanied by asearch score. For some visual queries, some of the search systems willfind no relevant results. For example, if the visual query was a pictureof a flower, the facial recognition search system and the bar codesearch system will not find any relevant results. In some embodiments,if no relevant results are found, a null or zero search score isreceived from that search system (216). In some embodiments, if thefront end server does not receive a result from a search system after apre-defined period of time (e.g., 0.2, 0.5, 1, 2 or 5 seconds), it willprocess the received results as if that timed out server produced a nullsearch score and will process the received results from the other searchsystems.

Optionally, when at least two of the received search results meetpre-defined criteria, they are ranked (218). In some embodiments, one ofthe predefined criteria excludes void results. A pre-defined criterionis that the results are not void. In some embodiments, one of thepredefined criteria excludes results having numerical score (e.g., for arelevance factor) that falls below a pre-defined minimum score.Optionally, the plurality of search results are filtered (220). In someembodiments, the results are only filtered if the total number ofresults exceeds a pre-defined threshold. In some embodiments, all theresults are ranked but the results falling below a pre-defined minimumscore are excluded. For some visual queries, the content of the resultsare filtered. For example, if some of the results contain privateinformation or personal protected information, these results arefiltered out.

Optionally, the visual query server system creates a compound searchresult (222). One embodiment of this is when more than one search systemresult is embedded in an interactive results document as explained withrespect to FIG. 3. The term query server system (118, FIG. 1) mayaugment the results from one of the parallel search systems with resultsfrom a term search, where the additional results are either links todocuments or information sources, or text and/or images containingadditional information that may be relevant to the visual query. Thus,for example, the compound search result may contain an OCR result and alink to a named entity in the OCR document (224).

In some embodiments, the OCR search system (112-B, FIG. 1) or the frontend visual query processing server (110, FIG. 1) recognizes likelyrelevant words in the text. For example, it may recognize named entitiessuch as famous people or places. The named entities are submitted asquery terms to the term query server system (118, FIG. 1). In someembodiments, the term query results produced by the term query serversystem are embedded in the visual query result as a “link.” In someembodiments, the term query results are returned as separate links. Forexample, if a picture of a book cover were the visual query, it islikely that an object recognition search system will produce a highscoring hit for the book. As such a term query for the title of the bookwill be run on the term query server system 118 and the term queryresults are returned along with the visual query results. In someembodiments, the term query results are presented in a labeled group todistinguish them from the visual query results. The results may besearched individually, or a search may be performed using all therecognized named entities in the search query to produce particularlyrelevant additional search results. For example, if the visual query isa scanned travel brochure about Paris, the returned result may includelinks to the term query server system 118 for initiating a search on aterm query “Notre Dame.” Similarly, compound search results includeresults from text searches for recognized famous images. For example, inthe same travel brochure, live links to the term query results forfamous destinations shown as pictures in the brochure like “EiffelTower” and “Louvre” may also be shown (even if the terms “Eiffel Tower”and “Louvre” did not appear in the brochure itself)

The visual query server system then sends at least one result to theclient system (226). Typically, if the visual query processing serverreceives a plurality of search results from at least some of theplurality of search systems, it will then send at least one of theplurality of search results to the client system. For some visualqueries, only one search system will return relevant results. Forexample, in a visual query containing only an image of text, only theOCR server's results may be relevant. For some visual queries, only oneresult from one search system may be relevant. For example, only theproduct related to a scanned bar code may be relevant. In theseinstances, the front end visual processing server will return only therelevant search result(s). For some visual queries, a plurality ofsearch results are sent to the client system, and the plurality ofsearch results include search results from more than one of the parallelsearch systems (228). This may occur when more than one distinct imageis in the visual query. For example, if the visual query were a pictureof a person riding a horse, results for facial recognition of the personcould be displayed along with object identification results for thehorse. In some embodiments, all the results for a particular query byimage search system are grouped and presented together. For example, thetop N facial recognition results are displayed under a heading “facialrecognition results” and the top N object recognition results aredisplayed together under a heading “object recognition results.”Alternatively, as discussed below, the search results from a particularimage search system may be grouped by image region. For example, if thevisual query includes two faces, both of which produce facialrecognition results, the results for each face would be presented as adistinct group. For some visual queries (e.g., a visual query includingan image of both text and one or more objects), the search results mayinclude both OCR results and one or more image-match results (230).

In some embodiments, the user may wish to learn more about a particularsearch result. For example, if the visual query was a picture of adolphin and the “image to terms” search system returns the followingterms “water,” “dolphin,” “blue,” and “Flipper;” the user may wish torun a text based query term search on “Flipper.” When the user wishes torun a search on a term query (e.g., as indicated by the user clicking onor otherwise selecting a corresponding link in the search results), thequery term server system (118, FIG. 1) is accessed, and the search onthe selected term(s) is run. The corresponding search term results aredisplayed on the client system either separately or in conjunction withthe visual query results (232). In some embodiments, the front endvisual query processing server (110, FIG. 1) automatically (i.e.,without receiving any user command, other than the initial visual query)chooses one or more top potential text results for the visual query,runs those text results on the term query server system 118, and thenreturns those term query results along with the visual query result tothe client system as a part of sending at least one search result to theclient system (232). In the example above, if “Flipper” was the firstterm result for the visual query picture of a dolphin, the front endserver runs a term query on “Flipper” and returns those term queryresults along with the visual query results to the client system. Thisembodiment, wherein a term result that is considered likely to beselected by the user is automatically executed prior to sending searchresults from the visual query to the user, saves the user time. In someembodiments, these results are displayed as a compound search result(222) as explained above. In other embodiments, the results are part ofa search result list instead of or in addition to a compound searchresult.

FIG. 3 is a flow diagram illustrating the process for responding to avisual query with an interactive results document. The first threeoperations (202, 210, 214) are described above with reference to FIG. 2.From the search results which are received from the parallel searchsystems (214), an interactive results document is created (302).

Creating the interactive results document (302) will now be described indetail. For some visual queries, the interactive results documentincludes one or more visual identifiers of respective sub-portions ofthe visual query. Each visual identifier has at least one userselectable link to at least one of the search results. A visualidentifier identifies a respective sub-portion of the visual query. Forsome visual queries, the interactive results document has only onevisual identifier with one user selectable link to one or more results.In some embodiments, a respective user selectable link to one or more ofthe search results has an activation region, and the activation regioncorresponds to the sub-portion of the visual query that is associatedwith a corresponding visual identifier.

In some embodiments, the visual identifier is a bounding box (304). Insome embodiments, the bounding box encloses a sub-portion of the visualquery as shown in FIG. 12A. The bounding box need not be a square orrectangular box shape but can be any sort of shape including circular,oval, conformal (e.g., to an object in, entity in or region of thevisual query), irregular or any other shape as shown in FIG. 12B. Forsome visual queries, the bounding box outlines the boundary of anidentifiable entity in a sub-portion of the visual query (306). In someembodiments, each bounding box includes a user selectable link to one ormore search results, where the user selectable link has an activationregion corresponding to a sub-portion of the visual query surrounded bythe bounding box. When the space inside the bounding box (the activationregion of the user selectable link) is selected by the user, searchresults that correspond to the image in the outlined sub-portion arereturned.

In some embodiments, the visual identifier is a label (307) as shown inFIG. 14. In some embodiments, label includes at least one termassociated with the image in the respective sub-portion of the visualquery. Each label is formatted for presentation in the interactiveresults document on or near the respective sub-portion. In someembodiments, the labels are color coded.

In some embodiments, each respective visual identifiers is formatted forpresentation in a visually distinctive manner in accordance with a typeof recognized entity in the respective sub-portion of the visual query.For example, as shown in FIG. 13, bounding boxes around a product, aperson, a trademark, and the two textual areas are each presented withdistinct cross-hatching patterns, representing differently coloredtransparent bounding boxes. In some embodiments, the visual identifiersare formatted for presentation in visually distinctive manners such asoverlay color, overlay pattern, label background color, label backgroundpattern, label font color, and border color.

In some embodiments, the user selectable link in the interactive resultsdocument is a link to a document or object that contains one or moreresults related to the corresponding sub-portion of the visual query(308). In some embodiments, at least one search result includes datarelated to the corresponding sub-portion of the visual query. As such,when the user selects the selectable link associated with the respectivesub-portion, the user is directed to the search results corresponding tothe recognized entity in the respective sub-portion of the visual query.

For example, if a visual query was a photograph of a bar code, there maybe portions of the photograph which are irrelevant parts of thepackaging upon which the bar code was affixed. The interactive resultsdocument may include a bounding box around only the bar code. When theuser selects inside the outlined bar code bounding box, the bar codesearch result is displayed. The bar code search result may include oneresult, the name of the product corresponding to that bar code, or thebar code results may include several results such as a variety of placesin which that product can be purchased, reviewed, etc.

In some embodiments, when the sub-portion of the visual querycorresponding to a respective visual identifier contains text comprisingone or more terms, the search results corresponding to the respectivevisual identifier include results from a term query search on at leastone of the terms in the text. In some embodiments, when the sub-portionof the visual query corresponding to a respective visual identifiercontains a person's face for which at least one match (i.e., searchresult) is found that meets predefined reliability (or other) criteria,the search results corresponding to the respective visual identifierinclude one or more of: name, handle, contact information, accountinformation, address information, current location of a related mobiledevice associated with the person whose face is contained in theselectable sub-portion, other images of the person whose face iscontained in the selectable sub-portion, and potential image matches forthe person's face. In some embodiments, when the sub-portion of thevisual query corresponding to a respective visual identifier contains aproduct for which at least one match (i.e., search result) is found thatmeets predefined reliability (or other) criteria, the search resultscorresponding to the respective visual identifier include one or moreof: product information, a product review, an option to initiatepurchase of the product, an option to initiate a bid on the product, alist of similar products, and a list of related products.

Optionally, a respective user selectable link in the interactive resultsdocument includes anchor text, which is displayed in the documentwithout having to activate the link. The anchor text providesinformation, such as a key word or term, related to the informationobtained when the link is activated. Anchor text may be displayed aspart of the label (307), or in a portion of a bounding box (304), or asadditional information displayed when a user hovers a cursor over a userselectable link for a pre-determined period of time such as 1 second.

Optionally, a respective user selectable link in the interactive resultsdocument is a link to a search engine for searching for information ordocuments corresponding to a text-based query (sometimes herein called aterm query). Activation of the link causes execution of the search bythe search engine, where the query and the search engine are specifiedby the link (e.g., the search engine is specified by a URL in the linkand the text-based search query is specified by a URL parameter of thelink), with results returned to the client system. Optionally, the linkin this example may include anchor text specifying the text or terms inthe search query.

In some embodiments, the interactive results document produced inresponse to a visual query can include a plurality of links thatcorrespond to results from the same search system. For example, a visualquery may be an image or picture of a group of people. The interactiveresults document may include bounding boxes around each person, whichwhen activated returns results from the facial recognition search systemfor each face in the group. For some visual queries, a plurality oflinks in the interactive results document corresponds to search resultsfrom more than one search system (310). For example, if a picture of aperson and a dog was submitted as the visual query, bounding boxes inthe interactive results document may outline the person and the dogseparately. When the person (in the interactive results document) isselected, search results from the facial recognition search system areretuned, and when the dog (in the interactive results document) isselected, results from the image-to-terms search system are returned.For some visual queries, the interactive results document contains anOCR result and an image match result (312). For example, if a picture ofa person standing next to a sign were submitted as a visual query, theinteractive results document may include visual identifiers for theperson and for the text in the sign. Similarly, if a scan of a magazinewas used as the visual query, the interactive results document mayinclude visual identifiers for photographs or trademarks inadvertisements on the page as well as a visual identifier for the textof an article also on that page.

After the interactive results document has been created, it is sent tothe client system (314). In some embodiments, the interactive resultsdocument (e.g., document 1200, FIG. 15) is sent in conjunction with alist of search results from one or more parallel search systems, asdiscussed above with reference to FIG. 2. In some embodiments, theinteractive results document is displayed at the client system above orotherwise adjacent to a list of search results from one or more parallelsearch systems (315) as shown in FIG. 15.

Optionally, the user will interact with the results document byselecting a visual identifier in the results document. The server systemreceives from the client system information regarding the user selectionof a visual identifier in the interactive results document (316). Asdiscussed above, in some embodiments, the link is activated by selectingan activation region inside a bounding box. In other embodiments, thelink is activated by a user selection of a visual identifier of asub-portion of the visual query, which is not a bounding box. In someembodiments, the linked visual identifier is a hot button, a labellocated near the sub-portion, an underlined word in text, or otherrepresentation of an object or subject in the visual query.

In embodiments where the search results list is presented with theinteractive results document (315), when the user selects a userselectable link (316), the search result in the search results listcorresponding to the selected link is identified. In some embodiments,the cursor will jump or automatically move to the first resultcorresponding to the selected link. In some embodiments in which thedisplay of the client 102 is too small to display both the interactiveresults document and the entire search results list, selecting a link inthe interactive results document causes the search results list toscroll or jump so as to display at least a first result corresponding tothe selected link. In some other embodiments, in response to userselection of a link in the interactive results document, the resultslist is reordered such that the first result corresponding to the linkis displayed at the top of the results list.

In some embodiments, when the user selects the user selectable link(316) the visual query server system sends at least a subset of theresults, related to a corresponding sub-portion of the visual query, tothe client for display to the user (318). In some embodiments, the usercan select multiple visual identifiers concurrently and will receive asubset of results for all of the selected visual identifiers at the sametime. In other embodiments, search results corresponding to the userselectable links are preloaded onto the client prior to user selectionof any of the user selectable links so as to provide search results tothe user virtually instantaneously in response to user selection of oneor more links in the interactive results document.

FIG. 4 is a flow diagram illustrating the communications between aclient and a visual query server system. The client 102 receives avisual query from a user/querier/requester (402). In some embodiments,visual queries can only be accepted from users who have signed up for or“opted in” to the visual query system. In some embodiments, searches forfacial recognition matches are only performed for users who have signedup for the facial recognition visual query system, while other types ofvisual queries are performed for anyone regardless of whether they have“opted in” to the facial recognition portion.

As explained above, the format of the visual query can take many forms.The visual query will likely contain one or more subjects located insub-portions of the visual query document. For some visual queries, theclient system 102 performs type recognition pre-processing on the visualquery (404). In some embodiments, the client system 102 searches forparticular recognizable patterns in this pre-processing system. Forexample, for some visual queries the client may recognize colors. Forsome visual queries the client may recognize that a particularsub-portion is likely to contain text (because that area is made up ofsmall dark characters surrounded by light space etc.) The client maycontain any number of pre-processing type recognizers, or typerecognition modules. In some embodiments, the client will have a typerecognition module (barcode recognition 406) for recognizing bar codes.It may do so by recognizing the distinctive striped pattern in arectangular area. In some embodiments, the client will have a typerecognition module (face detection 408) for recognizing that aparticular subject or sub-portion of the visual query is likely tocontain a face.

In some embodiments, the recognized “type” is returned to the user forverification. For example, the client system 102 may return a messagestating “a bar code has been found in your visual query, are youinterested in receiving bar code query results?” In some embodiments,the message may even indicate the sub-portion of the visual query wherethe type has been found. In some embodiments, this presentation issimilar to the interactive results document discussed with reference toFIG. 3. For example, it may outline a sub-portion of the visual queryand indicate that the sub-portion is likely to contain a face, and askthe user if they are interested in receiving facial recognition results.

After the client 102 performs the optional pre-processing of the visualquery, the client sends the visual query to the visual query serversystem 106, specifically to the front end visual query processing server110. In some embodiments, if pre-processing produced relevant results,i.e., if one of the type recognition modules produced results above acertain threshold, indicating that the query or a sub-portion of thequery is likely to be of a particular type (face, text, barcode etc.),the client will pass along information regarding the results of thepre-processing. For example, the client may indicate that the facerecognition module is 75% sure that a particular sub-portion of thevisual query contains a face. More generally, the pre-processingresults, if any, include one or more subject type values (e.g., barcode, face, text, etc.). Optionally, the pre-processing results sent tothe visual query server system include one or more of: for each subjecttype value in the pre-processing results, information identifying asub-portion of the visual query corresponding to the subject type value,and for each subject type value in the pre-processing results, aconfidence value indicating a level of confidence in the subject typevalue and/or the identification of a corresponding sub-portion of thevisual query.

The front end server 110 receives the visual query from the clientsystem (202). The visual query received may contain the pre-processinginformation discussed above. As described above, the front end serversends the visual query to a plurality of parallel search systems (210).If the front end server 110 received pre-processing informationregarding the likelihood that a sub-portion contained a subject of acertain type, the front end server may pass this information along toone or more of the parallel search systems. For example, it may pass onthe information that a particular sub-portion is likely to be a face sothat the facial recognition search system 112-A can process thatsubsection of the visual query first. Similarly, sending the sameinformation (that a particular sub-portion is likely to be a face) maybe used by the other parallel search systems to ignore that sub-portionor analyze other sub-portions first. In some embodiments, the front endserver will not pass on the pre-processing information to the parallelsearch systems, but will instead use this information to augment the wayin which it processes the results received from the parallel searchsystems.

As explained with reference to FIG. 2, for at some visual queries, thefront end server 110 receives a plurality of search results from theparallel search systems (214). The front end server may then perform avariety of ranking and filtering, and may create an interactive searchresult document as explained with reference to FIGS. 2 and 3. If thefront end server 110 received pre-processing information regarding thelikelihood that a sub-portion contained a subject of a certain type, itmay filter and order by giving preference to those results that matchthe pre-processed recognized subject type. If the user indicated that aparticular type of result was requested, the front end server will takethe user's requests into account when processing the results. Forexample, the front end server may filter out all other results if theuser only requested bar code information, or the front end server willlist all results pertaining to the requested type prior to listing theother results. If an interactive visual query document is returned, theserver may pre-search the links associated with the type of result theuser indicated interest in, while only providing links for performingrelated searches for the other subjects indicated in the interactiveresults document. Then the front end server 110 sends the search resultsto the client system (226).

The client 102 receives the results from the server system (412). Whenapplicable, these results will include the results that match the typeof result found in the pre-processing stage. For example, in someembodiments they will include one or more bar code results (414) or oneor more facial recognition results (416). If the client's pre-processingmodules had indicated that a particular type of result was likely, andthat result was found, the found results of that type will be listedprominently.

Optionally the user will select or annotate one or more of the results(418). The user may select one search result, may select a particulartype of search result, and/or may select a portion of an interactiveresults document (420). Selection of a result is implicit feedback thatthe returned result was relevant to the query. Such feedback informationcan be utilized in future query processing operations. An annotationprovides explicit feedback about the returned result that can also beutilized in future query processing operations. Annotations take theform of corrections of portions of the returned result (like acorrection to a mis-OCRed word) or a separate annotation (either freeform or structured.)

The user's selection of one search result, generally selecting the“correct” result from several of the same type (e.g., choosing thecorrect result from a facial recognition server), is a process that isreferred to as a selection among interpretations. The user's selectionof a particular type of search result, generally selecting the result“type” of interest from several different types of returned results(e.g., choosing the OCRed text of an article in a magazine rather thanthe visual results for the advertisements also on the same page), is aprocess that is referred to as disambiguation of intent. A user maysimilarly select particular linked words (such as recognized namedentities) in an OCRed document as explained in detail with reference toFIG. 8.

The user may alternatively or additionally wish to annotate particularsearch results. This annotation may be done in freeform style or in astructured format (422). The annotations may be descriptions of theresult or may be reviews of the result. For example, they may indicatethe name of subject(s) in the result, or they could indicate “this is agood book” or “this product broke within a year of purchase.” Anotherexample of an annotation is a user-drawn bounding box around asub-portion of the visual query and user-provided text identifying theobject or subject inside the bounding box. User annotations areexplained in more detail with reference to FIG. 5.

The user selections of search results and other annotations are sent tothe server system (424). The front end server 110 receives theselections and annotations and further processes them (426). If theinformation was a selection of an object, sub-region or term in aninteractive results document, further information regarding thatselection may be requested, as appropriate. For example, if theselection was of one visual result, more information about that visualresult would be requested. If the selection was a word (either from theOCR server or from the Image-to-Terms server) a textual search of thatword would be sent to the term query server system 118. If the selectionwas of a person from a facial image recognition search system, thatperson's profile would be requested. If the selection was for aparticular portion of an interactive search result document, theunderlying visual query results would be requested.

If the server system receives an annotation, the annotation is stored ina query and annotation database 116, explained with reference to FIG. 5.Then the information from the annotation database 116 is periodicallycopied to individual annotation databases for one or more of theparallel server systems, as discussed below with reference to FIGS.7-10.

FIG. 5 is a block diagram illustrating a client system 102 in accordancewith one embodiment of the present invention. The client system 102typically includes one or more processing units (CPU's) 702, one or morenetwork or other communications interfaces 704, memory 712, and one ormore communication buses 714 for interconnecting these components. Theclient system 102 includes a user interface 705. The user interface 705includes a display device 706 and optionally includes an input meanssuch as a keyboard, mouse, or other input buttons 708. Alternatively orin addition the display device 706 includes a touch sensitive surface709, in which case the display 706/709 is a touch sensitive display. Inclient systems that have a touch sensitive display 706/709, a physicalkeyboard is optional (e.g., a soft keyboard may be displayed whenkeyboard entry is needed). Furthermore, some client systems use amicrophone and voice recognition to supplement or replace the keyboard.Optionally, the client 102 includes a GPS (global positioning satellite)receiver, or other location detection apparatus 707 for determining thelocation of the client system 102. In some embodiments, visual querysearch services are provided that require the client system 102 toprovide the visual query server system to receive location informationindicating the location of the client system 102.

The client system 102 also includes an image capture device 710 such asa camera or scanner. Memory 712 includes high-speed random accessmemory, such as DRAM, SRAM, DDR RAM or other random access solid statememory devices; and may include non-volatile memory, such as one or moremagnetic disk storage devices, optical disk storage devices, flashmemory devices, or other non-volatile solid state storage devices.Memory 712 may optionally include one or more storage devices remotelylocated from the CPU(s) 702. Memory 712, or alternately the non-volatilememory device(s) within memory 712, comprises a non-transitory computerreadable storage medium. In some embodiments, memory 712 or the computerreadable storage medium of memory 712 stores the following programs,modules and data structures, or a subset thereof:

-   -   an operating system 716 that includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   a network communication module 718 that is used for connecting        the client system 102 to other computers via the one or more        communication network interfaces 704 (wired or wireless) and one        or more communication networks, such as the Internet, other wide        area networks, local area networks, metropolitan area networks,        and so on;    -   a image capture module 720 for processing a respective image        captured by the image capture device/camera 710, where the        respective image may be sent (e.g., by a client application        module) as a visual query to the visual query server system;    -   one or more client application modules 722 for handling various        aspects of querying by image, including but not limited to: a        query-by-image submission module 724 for submitting visual        queries to the visual query server system; optionally a region        of interest selection module 725 that detects a selection (such        as a gesture on the touch sensitive display 706/709) of a region        of interest in an image and prepares that region of interest as        a visual query; a results browser 726 for displaying the results        of the visual query; and optionally an annotation module 728        with optional modules for structured annotation text entry 730        such as filling in a form or for freeform annotation text entry        732, which can accept annotations from a variety of formats, and        an image region selection module 734 (sometimes referred to        herein as a result selection module) which allows a user to        select a particular sub-portion of an image for annotation;    -   an optional content authoring application(s) 736 that allow a        user to author a visual query by creating or editing an image        rather than just capturing one via the image capture device 710;        optionally, one or such applications 736 may include        instructions that enable a user to select a sub-portion of an        image for use as a visual query;    -   an optional local image analysis module 738 that pre-processes        the visual query before sending it to the visual query server        system. The local image analysis may recognize particular types        of images, or sub-regions within an image. Examples of image        types that may be recognized by such modules 738 include one or        more of: facial type (facial image recognized within visual        query), bar code type (bar code recognized within visual query),        and text type (text recognized within visual query); and    -   additional optional client applications 740 such as an email        application, a phone application, a browser application, a        mapping application, instant messaging application, social        networking application etc. In some embodiments, the application        corresponding to an appropriate actionable search result can be        launched or accessed when the actionable search result is        selected.

Optionally, the image region selection module 734 which allows a user toselect a particular sub-portion of an image for annotation, also allowsthe user to choose a search result as a “correct” hit withoutnecessarily further annotating it. For example, the user may bepresented with a top N number of facial recognition matches and maychoose the correct person from that results list. For some searchqueries, more than one type of result will be presented, and the userwill choose a type of result. For example, the image query may include aperson standing next to a tree, but only the results regarding theperson is of interest to the user. Therefore, the image selection module734 allows the user to indicate which type of image is the “correct”type—i.e., the type he is interested in receiving. The user may alsowish to annotate the search result by adding personal comments ordescriptive words using either the annotation text entry module 730 (forfilling in a form) or freeform annotation text entry module 732.

In some embodiments, the optional local image analysis module 738 is aportion of the client application (108, FIG. 1). Furthermore, in someembodiments the optional local image analysis module 738 includes one ormore programs to perform local image analysis to pre-process orcategorize the visual query or a portion thereof. For example, theclient application 722 may recognize that the image contains a bar code,a face, or text, prior to submitting the visual query to a searchengine. In some embodiments, when the local image analysis module 738detects that the visual query contains a particular type of image, themodule asks the user if they are interested in a corresponding type ofsearch result. For example, the local image analysis module 738 maydetect a face based on its general characteristics (i.e., withoutdetermining which person's face) and provides immediate feedback to theuser prior to sending the query on to the visual query server system. Itmay return a result like, “A face has been detected, are you interestedin getting facial recognition matches for this face?” This may save timefor the visual query server system (106, FIG. 1). For some visualqueries, the front end visual query processing server (110, FIG. 1) onlysends the visual query to the search system 112 corresponding to thetype of image recognized by the local image analysis module 738. Inother embodiments, the visual query to the search system 112 may sendthe visual query to all of the search systems 112A-N, but will rankresults from the search system 112 corresponding to the type of imagerecognized by the local image analysis module 738. In some embodiments,the manner in which local image analysis impacts on operation of thevisual query server system depends on the configuration of the clientsystem, or configuration or processing parameters associated with eitherthe user or the client system. Furthermore, the actual content of anyparticular visual query and the results produced by the local imageanalysis may cause different visual queries to be handled differently ateither or both the client system and the visual query server system.

In some embodiments, bar code recognition is performed in two steps,with analysis of whether the visual query includes a bar code performedon the client system at the local image analysis module 738. Then thevisual query is passed to a bar code search system only if the clientdetermines the visual query is likely to include a bar code. In otherembodiments, the bar code search system processes every visual query.

Optionally, the client system 102 includes additional clientapplications 740.

FIG. 6 is a block diagram illustrating a front end visual queryprocessing server system 110 in accordance with one embodiment of thepresent invention. The front end server 110 typically includes one ormore processing units (CPU's) 802, one or more network or othercommunications interfaces 804, memory 812, and one or more communicationbuses 814 for interconnecting these components. Memory 812 includeshigh-speed random access memory, such as DRAM, SRAM, DDR RAM or otherrandom access solid state memory devices; and may include non-volatilememory, such as one or more magnetic disk storage devices, optical diskstorage devices, flash memory devices, or other non-volatile solid statestorage devices. Memory 812 may optionally include one or more storagedevices remotely located from the CPU(s) 802. Memory 812, or alternatelythe non-volatile memory device(s) within memory 812, comprises anon-transitory computer readable storage medium. In some embodiments,memory 812 or the computer readable storage medium of memory 812 storesthe following programs, modules and data structures, or a subsetthereof:

-   -   an operating system 816 that includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   a network communication module 818 that is used for connecting        the front end server system 110 to other computers via the one        or more communication network interfaces 804 (wired or wireless)        and one or more communication networks, such as the Internet,        other wide area networks, local area networks, metropolitan area        networks, and so on;    -   a query manager 820 for handling the incoming visual queries        from the client system 102 and sending them to two or more        parallel search systems; as described elsewhere in this        document, in some special situations a visual query may be        directed to just one of the search systems, such as when the        visual query includes a client-generated instruction (e.g.,        “facial recognition search only”);    -   a results filtering module 822 for optionally filtering the        results from the one or more parallel search systems and sending        the top or “relevant” results to the client system 102 for        presentation;    -   a results ranking and formatting module 824 for optionally        ranking the results from the one or more parallel search systems        and for formatting the results for presentation;    -   a results document creation module 826, is used when        appropriate, to create an interactive search results document;        module 826 may include sub-modules, including but not limited to        a bounding box creation module 828 and a link creation module        830;    -   a label creation module 831 for creating labels that are visual        identifiers of respective sub-portions of a visual query;    -   an annotation module 832 for receiving annotations from a user        and sending them to an annotation database 116;    -   an actionable search results module 838 for generating, in        response to a visual query, one or more actionable search result        elements, each configured to launch a client-side action;        examples of actionable search result elements are buttons to        initiate a telephone call, to initiate email message, to map an        address, to make a restaurant reservation, and to provide an        option to purchase a product; and    -   a query and annotation database 116 which comprises the database        itself 834 and an index to the database 836.

The results ranking and formatting module 824 ranks the results returnedfrom the one or more parallel search systems (112-A-112-N, FIG. 1). Asalready noted above, for some visual queries, only the results from onesearch system may be relevant. In such an instance, only the relevantsearch results from that one search system are ranked. For some visualqueries, several types of search results may be relevant. In theseinstances, in some embodiments, the results ranking and formattingmodule 824 ranks all of the results from the search system having themost relevant result (e.g., the result with the highest relevance score)above the results for the less relevant search systems. In otherembodiments, the results ranking and formatting module 824 ranks a topresult from each relevant search system above the remaining results. Insome embodiments, the results ranking and formatting module 824 ranksthe results in accordance with a relevance score computed for each ofthe search results. For some visual queries, augmented textual queriesare performed in addition to the searching on parallel visual searchsystems. In some embodiments, when textual queries are also performed,their results are presented in a manner visually distinctive from thevisual search system results.

The results ranking and formatting module 824 also formats the results.In some embodiments, the results are presented in a list format. In someembodiments, the results are presented by means of an interactiveresults document. In some embodiments, both an interactive resultsdocument and a list of results are presented. In some embodiments, thetype of query dictates how the results are presented. For example, ifmore than one searchable subject is detected in the visual query, thenan interactive results document is produced, while if only onesearchable subject is detected the results will be displayed in listformat only.

The results document creation module 826 is used to create aninteractive search results document. The interactive search resultsdocument may have one or more detected and searched subjects. Thebounding box creation module 828 creates a bounding box around one ormore of the searched subjects. The bounding boxes may be rectangularboxes, or may outline the shape(s) of the subject(s). The link creationmodule 830 creates links to search results associated with theirrespective subject in the interactive search results document. In someembodiments, clicking within the bounding box area activates thecorresponding link inserted by the link creation module.

The query and annotation database 116 contains information that can beused to improve visual query results. In some embodiments, the user mayannotate the image after the visual query results have been presented.Furthermore, in some embodiments the user may annotate the image beforesending it to the visual query search system. Pre-annotation may helpthe visual query processing by focusing the results, or running textbased searches on the annotated words in parallel with the visual querysearches. In some embodiments, annotated versions of a picture can bemade public (e.g., when the user has given permission for publication,for example by designating the image and annotation(s) as not private),so as to be returned as a potential image match hit. For example, if auser takes a picture of a flower and annotates the image by givingdetailed genus and species information about that flower, the user maywant that image to be presented to anyone who performs a visual queryresearch looking for that flower. In some embodiments, the informationfrom the query and annotation database 116 is periodically pushed to theparallel search systems 112, which incorporate relevant portions of theinformation (if any) into their respective individual databases 114.

FIG. 7 is a block diagram illustrating one of the parallel searchsystems utilized to process a visual query. FIG. 7 illustrates a“generic” server system 112-N in accordance with one embodiment of thepresent invention. This server system is generic only in that itrepresents any one of the visual query search servers 112-N. The genericserver system 112-N typically includes one or more processing units(CPU's) 502, one or more network or other communications interfaces 504,memory 512, and one or more communication buses 514 for interconnectingthese components. Memory 512 includes high-speed random access memory,such as DRAM, SRAM, DDR RAM or other random access solid state memorydevices; and may include non-volatile memory, such as one or moremagnetic disk storage devices, optical disk storage devices, flashmemory devices, or other non-volatile solid state storage devices.Memory 512 may optionally include one or more storage devices remotelylocated from the CPU(s) 502. Memory 512, or alternately the non-volatilememory device(s) within memory 512, comprises a non-transitory computerreadable storage medium. In some embodiments, memory 512 or the computerreadable storage medium of memory 512 stores the following programs,modules and data structures, or a subset thereof:

-   -   an operating system 516 that includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   a network communication module 518 that is used for connecting        the generic server system 112-N to other computers via the one        or more communication network interfaces 504 (wired or wireless)        and one or more communication networks, such as the Internet,        other wide area networks, local area networks, metropolitan area        networks, and so on;    -   a search application 520 specific to the particular server        system, it may for example be a bar code search application, a        color recognition search application, a product recognition        search application, an object-or-object category search        application, or the like;    -   an optional index 522 if the particular search application        utilizes an index;    -   an optional image database 524 for storing the images relevant        to the particular search application, where the image data        stored, if any, depends on the search process type;    -   an optional results ranking module 526 (sometimes called a        relevance scoring module) for ranking the results from the        search application, the ranking module may assign a relevancy        score for each result from the search application, and if no        results reach a pre-defined minimum score, may return a null or        zero value score to the front end visual query processing server        indicating that the results from this server system are not        relevant; and    -   an annotation module 528 for receiving annotation information        from an annotation database (116, FIG. 1) determining if any of        the annotation information is relevant to the particular search        application and incorporating any determined relevant portions        of the annotation information into the respective annotation        database 530.

FIG. 8 is a block diagram illustrating an OCR search system 112-Butilized to process a visual query in accordance with one embodiment ofthe present invention. The OCR search system 112-B typically includesone or more processing units (CPU's) 602, one or more network or othercommunications interfaces 604, memory 612, and one or more communicationbuses 614 for interconnecting these components. Memory 612 includeshigh-speed random access memory, such as DRAM, SRAM, DDR RAM or otherrandom access solid state memory devices; and may include non-volatilememory, such as one or more magnetic disk storage devices, optical diskstorage devices, flash memory devices, or other non-volatile solid statestorage devices. Memory 612 may optionally include one or more storagedevices remotely located from the CPU(s) 602. Memory 612, or alternatelythe non-volatile memory device(s) within memory 612, comprises anon-transitory computer readable storage medium. In some embodiments,memory 612 or the computer readable storage medium of memory 612 storesthe following programs, modules and data structures, or a subsetthereof:

-   -   an operating system 616 that includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   a network communication module 618 that is used for connecting        the OCR search system 112-B to other computers via the one or        more communication network interfaces 604 (wired or wireless)        and one or more communication networks, such as the Internet,        other wide area networks, local area networks, metropolitan area        networks, and so on;    -   an Optical Character Recognition (OCR) module 620 which tries to        recognize text in the visual query, and converts the images of        letters into characters;    -   an optional OCR database 114-B which is utilized by the OCR        module 620 to recognize particular fonts, text patterns, and        other characteristics unique to letter recognition;    -   an optional spell check module 622 which improves the conversion        of images of letters into characters by checking the converted        words against a dictionary and replacing potentially        mis-converted letters in words that otherwise match a dictionary        word;    -   an optional named entity recognition module 624 which searches        for named entities within the converted text, sends the        recognized named entities as terms in a term query to the term        query server system (118, FIG. 1), and provides the results from        the term query server system as links embedded in the OCRed text        associated with the recognized named entities;    -   an optional text match application 632 which improves the        conversion of images of letters into characters by checking        converted segments (such as converted sentences and paragraphs)        against a database of text segments and replacing potentially        mis- converted letters in OCRed text segments that otherwise        match a text match application text segment, in some embodiments        the text segment found by the text match application is provided        as a link to the user (for example, if the user scanned one page        of the New York Times, the text match application may provide a        link to the entire posted article on the New York Times        website);    -   a results ranking and formatting module 626 for formatting the        OCRed results for presentation and formatting optional links to        named entities, and also optionally ranking any related results        from the text match application; and    -   an optional annotation module 628 for receiving annotation        information from an annotation database (116, FIG. 1)        determining if any of the annotation information is relevant to        the OCR search system and incorporating any determined relevant        portions of the annotation information into the respective        annotation database 630.

FIG. 9 is a block diagram illustrating a facial recognition searchsystem 112-A utilized to process a visual query with at least one facialimage in accordance with one embodiment of the present invention. Thefacial recognition search system 112-A typically includes one or moreprocessing units (CPU's) 902, one or more network or othercommunications interfaces 904, memory 912, and one or more communicationbuses 914 for interconnecting these components. Memory 912 includeshigh-speed random access memory, such as DRAM, SRAM, DDR RAM or otherrandom access solid state memory devices; and may include non-volatilememory, such as one or more magnetic disk storage devices, optical diskstorage devices, flash memory devices, or other non-volatile solid statestorage devices. Memory 912 may optionally include one or more storagedevices remotely located from the CPU(s) 902. Memory 912, or alternatelythe non-volatile memory device(s) within memory 912, comprises anon-transitory computer readable storage medium. In some embodiments,memory 912 or the computer readable storage medium of memory 912 storesthe following programs, modules and data structures, or a subset thereof

-   -   An operating system 916 that includes procedures for handling        various basic system services and for performing hardware        dependent tasks.    -   A network communication module 918 that is used for connecting        the facial recognition search system 112-A to other computers        via the one or more communication network interfaces 904 (wired        or wireless) and one or more communication networks, such as the        Internet, other wide area networks, local area networks,        metropolitan area networks, and so on.    -   A facial recognition search application 920 including a visual        identifier module 924 for identifying potential image matches        that potentially match a facial image in the query, a personal        identifier module 926 for identifying persons associated with        the potential image matches, and a social connection metrics        module 928 for retrieving person-specific data comprising        metrics of social connectivity to the requester (and/or another        person in the image), and a ranking module 930 for generating a        ranked list of identified persons in accordance with metrics of        visual similarity between the facial image and the potential        matches as well as in accordance with social connection metrics.    -   A facial image database 114-A, which is searched to find the        images that potentially match a facial image in a query,        includes one or more image sources such as social network images        932, web album images 934, photo sharing images 936, and        previous query images 938. The image sources used in response to        a particular query are identified in accordance with data        regarding the requester. In some embodiments, they include only        images in accounts belonging to or associated with the        requester, such as social networking accounts of the requester,        web albums of the requester, and so on. In other embodiments the        sources include images belonging to or associated with other        people with whom the requester is socially connected, e.g.,        people with a direct connection to a requester on a social        graph. Optionally, the facial image database 114-A includes        images of famous people 940. In some embodiments, the facial        image database includes facial images obtained from external        sources, such as vendors of facial images that are legally in        the public domain.    -   An image feature extractor 942 extracts characteristics derived        from images in the facial image database 114-A and stores the        information in a database of person- specific data 964. In some        embodiments, visual characteristics such as an indoor habitat        factor, an outdoor habitat factor, a gender factor, a race        factor, a glasses factor, a facial hair factor, a head hair        factor, a headwear factor, an eye color factor, occurrence        information, and co-occurrence information are extracted with a        visual features extractor 944. In some embodiments, metadata        characteristics such as date information, time information, and        location information are extracted with a metadata features        extractor 946,    -   Public databases 948 are sources of person-specific data, which        include connection metrics of social connectivity between the        person associated with a potential image match and the        requester. The data is obtained from a plurality of applications        including, but are not limited to, social network databases 922,        social microblog databases 950, blog databases 952, email        databases 954, IM databases 956, calendar databases 958, contact        lists 960, and/or public URLs 962.    -   A database of person-specific data 964 that stores information        specific to particular persons. Some or all of the        person-specific data is obtained from public databases. The        person-specific data is described in more detail with respect to        FIGS. 18A-C.    -   A results formatting module 966 for formatting the results for        presentation; in some embodiments, the formatted results include        the potential image matches and subset of information from the        database of person-specific data 964.    -   An annotation module 968 for receiving annotation information        from an annotation database (116, FIG. 1), for determining if        any of the annotation information is relevant to the facial        recognition search system, and for storing any determined        relevant portions of the annotation information into the        respective annotation database 970.    -   A person location module 972 acquires location information        concerning the current location of the requester and one or more        persons identified as potential matches to a facial image in a        visual query. The acquisition of location information by person        location module 972 and the use of location information to        improve matching of a person to a facial image by search        application 920 is discussed below with reference to FIGS. 16A,        17, 18A and 18C.

FIG. 10 is a block diagram illustrating an image-to-terms search system112- C utilized to process a visual query in accordance with oneembodiment of the present invention. In some embodiments, theimage-to-terms search system recognizes objects (instance recognition)in the visual query. In other embodiments, the image-to-terms searchsystem recognizes object categories (type recognition) in the visualquery. In some embodiments, the image to terms system recognizes bothobjects and object-categories. The image-to-terms search system returnspotential term matches for images in the visual query. Theimage-to-terms search system 112-C typically includes one or moreprocessing units (CPU's) 1002, one or more network or othercommunications interfaces 1004, memory 1012, and one or morecommunication buses 1014 for interconnecting these components. Memory1012 includes high-speed random access memory, such as DRAM, SRAM, DDRRAM or other random access solid state memory devices; and may includenon-volatile memory, such as one or more magnetic disk storage devices,optical disk storage devices, flash memory devices, or othernon-volatile solid state storage devices. Memory 1012 may optionallyinclude one or more storage devices remotely located from the CPU(s)1002. Memory 1012, or alternately the non-volatile memory device(s)within memory 1012, comprises a non-transitory computer readable storagemedium. In some embodiments, memory 1012 or the computer readablestorage medium of memory 1012 stores the following programs, modules anddata structures, or a subset thereof:

-   -   an operating system 1016 that includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   a network communication module 1018 that is used for connecting        the image-to-terms search system 112-C to other computers via        the one or more communication network interfaces 1004 (wired or        wireless) and one or more communication networks, such as the        Internet, other wide area networks, local area networks,        metropolitan area networks, and so on;    -   a image-to-terms search application 1020 that searches for        images matching the subject or subjects in the visual query in        the image search database 114-C;    -   an image search database 114-C which can be searched by the        search application 1020 to find images similar to the subject(s)        of the visual query;    -   a terms-to-image inverse index 1022, which stores the textual        terms used by users when searching for images using a text based        query search engine 1006;    -   a results ranking and formatting module 1024 for ranking the        potential image matches and/or ranking terms associated with the        potential image matches identified in the terms-to-image inverse        index 1022; and    -   an annotation module 1026 for receiving annotation information        from an annotation database (116, FIG. 1) determining if any of        the annotation information is relevant to the image-to terms        search system 112-C and storing any determined relevant portions        of the annotation information into the respective annotation        database 1028.

FIGS. 5-10 are intended more as functional descriptions of the variousfeatures which may be present in a set of computer systems than as astructural schematic of the embodiments described herein. In practice,and as recognized by those of ordinary skill in the art, items shownseparately could be combined and some items could be separated. Forexample, some items shown separately in these figures could beimplemented on single servers and single items could be implemented byone or more servers. The actual number of systems used to implementvisual query processing and how features are allocated among them willvary from one implementation to another.

Each of the methods described herein may be governed by instructionsthat are stored in a non-transitory computer readable storage medium andthat are executed by one or more processors of one or more servers orclients. The above identified modules or programs (i.e., sets ofinstructions) need not be implemented as separate software programs,procedures or modules, and thus various subsets of these modules may becombined or otherwise re-arranged in various embodiments. Each of theoperations shown in FIGS. 5-10 may correspond to instructions stored ina computer memory or non-transitory computer readable storage medium.

FIG. 11 illustrates a client system 102 with a screen shot of anexemplary visual query 1102. The client system 102 shown in FIG. 11 is amobile device such as a cellular telephone, portable music player, orportable emailing device. The client system 102 includes a display 706and one or more input means 708 such the buttons shown in this figure.In some embodiments, the display 706 is a touch sensitive display 709.In embodiments having a touch sensitive display 709, soft buttonsdisplayed on the display 709 may optionally replace some or all of theelectromechanical buttons 708. Touch sensitive displays are also helpfulin interacting with the visual query results as explained in more detailbelow. The client system 102 also includes an image capture mechanismsuch as a camera 710.

FIG. 11 illustrates a visual query 1102 which is a photograph or videoframe of a package on a shelf of a store. In the embodiments describedhere, the visual query is a two dimensional image having a resolutioncorresponding to the size of the visual query in pixels in each of twodimensions. The visual query 1102 in this example is a two dimensionalimage of three dimensional objects. The visual query 1102 includesbackground elements, a product package 1104, and a variety of types ofentities on the package including an image of a person 1106, an image ofa trademark 1108, an image of a product 1110, and a variety of textualelements 1112.

As explained with reference to FIG. 3, the visual query 1102 is sent tothe front end server 110, which sends the visual query 1102 to aplurality of parallel search systems (112A-N), receives the results andcreates an interactive results document.

FIGS. 12A and 12B each illustrate a client system 102 with a screen shotof an embodiment of an interactive results document 1200. Theinteractive results document 1200 includes one or more visualidentifiers 1202 of respective sub-portions of the visual query 1102,which each include a user selectable link to a subset of search results.FIGS. 12A and 12B illustrate an interactive results document 1200 withvisual identifiers that are bounding boxes 1202 (e.g., bounding boxes1202-1, 1202-2, 1202-3). In the embodiments shown in FIGS. 12A and 12B,the user activates the display of the search results corresponding to aparticular sub-portion by tapping on the activation region inside thespace outlined by its bounding box 1202. For example, the user wouldactivate the search results corresponding to the image of the person, bytapping on a bounding box 1306 (FIG. 13) surrounding the image of theperson. In other embodiments, the selectable link is selected using amouse or keyboard rather than a touch sensitive display. In someembodiments, the first corresponding search result is displayed when auser previews a bounding box 1202 (i.e., when the user single clicks,taps once, or hovers a pointer over the bounding box). The useractivates the display of a plurality of corresponding search resultswhen the user selects the bounding box (i.e., when the user doubleclicks, taps twice, or uses another mechanism to indicate selection.)

In FIGS. 12A and 12B the visual identifiers are bounding boxes 1202surrounding sub-portions of the visual query. FIG. 12A illustratesbounding boxes 1202 that are square or rectangular. FIG. 12B illustratesa bounding box 1202 that outlines the boundary of an identifiable entityin the sub-portion of the visual query, such as the bounding box 1202-3for a drink bottle. In some embodiments, a respective bounding box 1202includes smaller bounding boxes 1202 within it. For example, in FIGS.12A and 12B, the bounding box identifying the package 1202-1 surroundsthe bounding box identifying the trademark 1202-2 and all of the otherbounding boxes 1202. In some embodiments that include text, also includeactive hot links 1204 for some of the textual terms. FIG. 12B shows anexample where “Active Drink” and “United States” are displayed as hotlinks 1204. The search results corresponding to these terms are theresults received from the term query server system 118, whereas theresults corresponding to the bounding boxes are results from the queryby image search systems.

FIG. 13 illustrates a client system 102 with a screen shot of aninteractive results document 1200 that is coded by type of recognizedentity in the visual query. The visual query of FIG. 11 contains animage of a person 1106, an image of a trademark 1108, an image of aproduct 1110, and a variety of textual elements 1112. As such theinteractive results document 1200 displayed in FIG. 13 includes boundingboxes 1202 around a person 1306, a trademark 1308, a product 1310, andthe two textual areas 1312. The bounding boxes of FIG. 13 are eachpresented with separate cross-hatching which represents differentlycolored transparent bounding boxes 1202. In some embodiments, the visualidentifiers of the bounding boxes (and/or labels or other visualidentifiers in the interactive results document 1200) are formatted forpresentation in visually distinctive manners such as overlay color,overlay pattern, label background color, label background pattern, labelfont color, and bounding box border color. The type coding forparticular recognized entities is shown with respect to bounding boxesin FIG. 13, but coding by type can also be applied to visual identifiersthat are labels.

FIG. 14 illustrates a client device 102 with a screen shot of aninteractive results document 1200 with labels 1402 being the visualidentifiers of respective sub-portions of the visual query 1102 of FIG.11. The label visual identifiers 1402 each include a user selectablelink to a subset of corresponding search results. In some embodiments,the selectable link is identified by descriptive text displayed withinthe area of the label 1402. Some embodiments include a plurality oflinks within one label 1402. For example, in FIG. 14, the label hoveringover the image of a woman drinking includes a link to facial recognitionresults for the woman and a link to image recognition results for thatparticular picture (e.g., images of other products or advertisementsusing the same picture.)

In FIG. 14, the labels 1402 are displayed as partially transparent areaswith text that are located over their respective sub-portions of theinteractive results document. In other embodiments, a respective labelis positioned near but not located over its respective sub-portion ofthe interactive results document. In some embodiments, the labels arecoded by type in the same manner as discussed with reference to FIG. 13.In some embodiments, the user activates the display of the searchresults corresponding to a particular sub-portion corresponding to alabel 1302 by tapping on the activation region inside the space outlinedby the edges or periphery of the label 1302. The same previewing andselection functions discussed above with reference to the bounding boxesof FIGS. 12A and 12B also apply to the visual identifiers that arelabels 1402.

FIG. 15 illustrates a screen shot of an interactive results document1200 and the original visual query 1102 displayed concurrently with aresults list 1500. In some embodiments, the interactive results document1200 is displayed by itself as shown in FIGS. 12-14. In otherembodiments, the interactive results document 1200 is displayedconcurrently with the original visual query as shown in FIG. 15. In someembodiments, the list of visual query results 1500 is concurrentlydisplayed along with the original visual query 1102 and/or theinteractive results document 1200. The type of client system and theamount of room on the display 706 may determine whether the list ofresults 1500 is displayed concurrently with the interactive resultsdocument 1200. In some embodiments, the client system 102 receives (inresponse to a visual query submitted to the visual query server system)both the list of results 1500 and the interactive results document 1200,but only displays the list of results 1500 when the user scrolls belowthe interactive results document 1200. In some of these embodiments, theclient system 102 displays the results corresponding to a user selectedvisual identifier 1202/1402 without needing to query the server againbecause the list of results 1500 is received by the client system 102 inresponse to the visual query and then stored locally at the clientsystem 102.

In some embodiments, the list of results 1500 is organized intocategories 1502. Each category contains at least one result 1503. Insome embodiments, the categories titles are highlighted to distinguishthem from the results 1503. The categories 1502 are ordered according totheir calculated category weight. In some embodiments, the categoryweight is a combination of the weights of the highest N results in thatcategory. As such, the category that has likely produced more relevantresults is displayed first. In embodiments where more than one category1502 is returned for the same recognized entity (such as the facialimage recognition match and the image match shown in FIG. 15) thecategory displayed first has a higher category weight.

As explained with respect to FIG. 3, in some embodiments, when aselectable link in the interactive results document 1200 is selected bya user of the client system 102, the cursor will automatically move tothe appropriate category 1502 or to the first result 1503 in thatcategory. Alternatively, when a selectable link in the interactiveresults document is selected by a user of the client system 102, thelist of results 1500 is re-ordered such that the category or categoriesrelevant to the selected link are displayed first. This is accomplished,for example, by either coding the selectable links with informationidentifying the corresponding search results, or by coding the searchresults to indicate the corresponding selectable links or to indicatethe corresponding result categories.

In some embodiments, the categories of the search results correspond tothe query-by-image search system that produce those search results. Forexample, in FIG. 15 some of the categories are product match 1506, logomatch 1508, facial recognition match 1510, image match 1512. Theoriginal visual query 1102 and/or an interactive results document 1200may be similarly displayed with a category title such as the query 1504.Similarly, results from any term search performed by the term queryserver may also be displayed as a separate category, such as web results1514. In other embodiments, more than one entity in a visual query willproduce results from the same query-by-image search system. For example,the visual query could include two different faces that would returnseparate results from facial recognition search system 112-A. As such,in some embodiments, the categories 1502 are divided by recognizedentity rather than by search system. In some embodiments, an image ofthe recognized entity is displayed in the recognized entity categoryheader 1502 such that the results for that recognized entity aredistinguishable from the results for another recognized entity, eventhough both results are produced by the same query by image searchsystem. For example, in FIG. 15, the product match category 1506includes two entity product entities and as such as two entitycategories 1502—a boxed product 1516 and a bottled product 1518, each ofwhich have a plurality of corresponding search results 1503. In someembodiments, the categories may be divided by recognized entities andtype of query-by-image system. For example, in FIG. 15, there are twoseparate entities that returned relevant results under the product matchcategory product.

In some embodiments, the results 1503 include thumbnail images. Forexample, as shown for the facial recognition match results in FIG. 15,small versions (also called thumbnail images) of the pictures of thefacial matches for “Actress X” and “Social Network Friend Y” aredisplayed along with some textual description such as the name of theperson in the image.

FIGS. 16A-16B are flowcharts illustrating a process of responding to avisual query including a facial image, in accordance with someembodiments. Each of the operations shown these figures may correspondto instructions stored in a computer memory or non-transitory computerreadable storage medium. Facial recognition search system 112-Areceives, from a requester, a visual query with one or more facialimages in it (1602). In some embodiments, the fact that the visual querycontains at least one face is determined by the front end visual queryprocessing server 110. In other words, when a visual query is processedby facial recognition search system 112-A, at least a portion of thevisual query image has been determined to contain a potential face. Insome circumstances, the visual query contains a plurality of faces, suchas a picture of two or more friends, or a group photo of several people.In some cases where the visual query comprises a plurality of facialimages, the requester may only be interested in one of the faces. Assuch, in some embodiments when the visual query includes at least arespective facial image and a second facial image, prior to identifyingpotential image matches, the system receives a selection of therespective facial image from the requester. For example, in someembodiments the system identifies each potential face and requestsconfirmation regarding which face(s) in the query the requester wishesto have identified.

Images that potentially match a respective facial image are identified(1604). These images are called potential image matches. The potentialimage matches are identified in accordance with visual similaritycriteria. Also, the potential image matches are identified from one ormore image sources identified in accordance with data regarding therequester (1606). In some embodiments, data regarding the requester isobtained from a requester's profile information. In some embodiments,the requester's profile information is obtained from the requesterdirectly. Alternatively, or in addition, the requester's profileinformation is received from a social network. The potential imagematches include images that are tagged, i.e., images that includepersonal identifiers for the person or persons in the images. In someembodiments, the one or more image sources include images from arequestor's social networking database(s), web album(s), photo sharingdatabase(s), and other sources of images associated with the requester.Furthermore, in some embodiments, a database (940, FIG. 9) of famouspeople's images is also included in the image sources searched forpotential image matches. In some embodiments, the image sources searchedfor potential image matches also include images from the requestor'sfriends' or contacts' social networking database(s), web album(s), photosharing database(s), and other sources of images associated with therequester. In embodiments that include images from a requestor'sfriends' or contacts' databases, a determination of which databases toinclude is made. For example, in some embodiments, databases of apre-determined maximum number of friends or contacts is included. Inother embodiments, databases of only direct social networking friendsare included.

Then one or more persons associated with the potential image matches areidentified (1608). In some embodiments, the one or more persons areidentified from personal identifier tags associated with the identifiedimage matches. For example, the system may identify that Bob Smith, JoeJones, and Peter Johnson are persons associated with potential imagematches for a query including an image of a male friend because thesethree people were tagged in other images associated with the requestor,and these three people are visually similar to the facial image in thequery.

For each identified person, person-specific data is retrieved, whichincludes social connection metrics obtained from a plurality ofapplications (1610). The plurality of applications includescommunication applications, social networking applications, calendarapplications, and collaborative applications (1612). For example, theapplications may include applications such a Facebook, Twitter, Buzz,G-mail (email and IM), web calendars, blogs such as “LiveJournal”,personal public URLs, and any contact lists associated with them. Insome embodiments, data is obtained only from “public” publishedinformation on these applications. In other embodiments, data isobtained if it belongs to or has been explicitly shared with therequestor. In some embodiments, the person-specific data includes name,address, occupation, group memberships, interests, age, hometown,personal statistics, and work information for the respective identifiedperson (as discussed with more detail with respect to FIG. 18A). In someembodiments, this information is gleaned from one or more of the abovementioned applications.

The person-specific data includes social connection metrics, which aremetrics of social connectivity between the respective identified personand the requester (1614). In some embodiments, the social connectivitymetrics include metrics of social connectivity over one or more of theabove mentioned applications. For example, the social connectivitymetrics may take into account one or more of: whether the respectiveidentified person and the requestor are friends on a social networkingwebsite, the quantity (if any) of email and/or IM messages exchanged bythe requestor and the respective identified person, and whether therequester and the respective identified person follow each other'ssocial micro-blog posts, etc.

In some embodiments, the person-specific data for a respectiveidentified person also includes characteristics derived from otherimages of the respective person (1616). In some embodiments, thesecharacteristics include metadata information from the images such asdate information, time information, and location information. In otherembodiments, the characteristics derived from other images of therespective person comprises visual factors such as an indoor habitatfactor, an outdoor habitat factor, a gender factor, a race factor, aglasses factor, a facial hair factor, a head hair factor, a headwearfactor, and an eye color factor. In yet other embodiments,characteristics derived from other images of the respective personinclude occurrences information regarding an amount of occurrences ofthe respective person in the one or more image sources, and/orinformation regarding an amount of co-occurrences of the respectiveperson and with a second person in images from the one or more imagesources.

Optionally, in some embodiments, the current location information forthe requester and current location information for a respectiveidentified person are obtained (1618) by person location module 972(FIG. 9). For example, the current location of either the requester orthe respective identified person may be obtained from a GPS receiverlocated in a mobile device, from an IP address of desktop device used bythe person, from a home address or work address or the person, or from apublished location of the person (such as, “I am currently at aconference in Boston”).

Then an ordered list of persons is generated by ranking the one or moreidentified persons in accordance with one or more metrics of visualsimilarity between the respective facial image and the potential imagematches and also in accordance with ranking information comprising atleast the social connection metrics (1620). These and other factorsaffecting the ranking are discussed in more detail below with respect toFIG. 17.

The process continues as shown in FIG. 16B. Optionally, an opt-in listis checked to and a determination is made as to whether one or moreperson identifiers are releasable to the requestor (1622). In someembodiments, this check is done when the potentially matching image(s)are from a source other than the requester's own account(s), or when therequestor's own accounts do not contain tagged images of the respectiveidentified person.

Then the requester is sent at least one person identifier from theordered list (1624), thereby identifying one or more persons. In someembodiments, the person identifier is a name. In other embodiments, theperson identifier is a handle, email address, nickname or the like. Insome embodiments, a representative picture, such as a profile picture,an image of the identified person that best matches the visual query issent along with the person identifier. In such embodiments, when morethan one person is identified as a potential match, a representativepicture of each identified person is sent along with the response to theimage query. In some embodiments, additional information such as contactinformation, or a snippet of a recent public post is also sent with theperson identifier. In other embodiments, in addition to the personidentifier, the connection found between the requester and the person inthe image is also returned. For example, a ranked result of Joe Smith,could include the statement “Joe Smith is listed as a contact in morethan one of your accounts,” or “You and Joe Smith are both members ofthe Palo Alto Tennis Club” or “You and Joe Smith are both friends withKaren Jones.” Further information such as the person's contactinformation, group affiliations, the names of the people in-between therequester and the person in the matched image according to the socialgraph may be included in the results returned to the requester. In someembodiments, the augmented information presented to the requester isexplicitly or implicitly specified by the requester (e.g., byconfiguration values in his profile, or by parameters in the visualquery, or by the type of the visual query). In some embodiments, whenmore than one person identifier is sent to the requester, moreinformation is provided for the top ranked identified persons than forthe lower ranked identified persons.

In some embodiments, a copy of the visual query (or portion of the querywith the respective facial image) is also sent with the one or moreperson identifiers (1626). When more than one facial image was in theoriginal visual query and one or more facial images are positivelyidentified, in some embodiments, a copy of the visual query is also sentto one or more of the identified people in the visual query. Thus, if agroup photo is taken, and multiple people want copies of it, therequester does not to have find contact information for them andmanually send them a copy of the photograph. In some embodiments, arequester must first verify that copies should be sent to one or more ofthe identified people before they are sent.

In some embodiments, a selection of a personal identifier is receivedfrom the requester (1628). Then, in response to the selection, datacorresponding to the selected person identifier is sent to the requester(1630). In some embodiments this data includes one or more imagesassociated with the person identifier, contact information associatedwith the person identifier, public profile information associated withthe person identifier, etc. In some embodiments, the requester is giventhe option to store some or all of this information in the requester'scontact list, or to update the requester's contact information for theidentified person. In some embodiments, the information is associatedwith the requestor's visual query, or the portion of the query with thefacial image corresponding to the person identifier is stored withcontact list information.

Furthermore, in some embodiments, the facial image of the visual queryis stored as an additional image of a respective person corresponding tothe selected person identifier (1632). In some embodiments, the image isstored in a previous queries portion of the image sources (938, FIG. 9).In some embodiments, the requester is given an opportunity to annotatethe image to include additional data. In instances where annotation datais entered by the requester, it is received and stored (1634) by facialrecognition search system 112-A. The annotation module (968, FIG. 9)accepts annotations to improve future facial recognition searches. Forexample, if the user annotates a picture of a person with the name ofthat person, that picture might be used in future facial recognitionqueries to recognize the person. In some embodiments, for privacyreasons, the additional annotated pictures of a person may be used byfacial recognition search system 112-A to augment the facial recognitionprocess but are not returned as an image result to anyone but theoriginal requester. In some embodiments, only the actual personidentified in the visual query is allowed to make an image public (oravailable to people other than the requester). In some embodiments, oncethe person is positively identified, a request is sent to that personasking them if they will allow the image to be returned a result forfuture queries for people within their social network.

In some embodiments, more than one image of the same person may beretrieved at step 1604. Once the potential matching images are retrievedand it is determined that the images are of the same person, which maybe done by noting that the images both have the same personal ID, sameor similar personal-specific data (name, address, and the like) or havesame or similar social connections, the images will be associated withthe same data and treated like a single unit for the rest of theprocessing steps. Optionally, if two or more images are returned withthe same person identifier in step 1624, more than one retrieved imagefor the same person identifier are returned in the response to the imagequery.

FIG. 17 is a flowchart illustrating factors and characteristics used ingenerating an ordered list of persons that potentially match a facialimage in a visual query. This flowchart provides more informationregarding step 1620, discussed above.

In some embodiments, various factors are used in determining a rankingscore for a respective person in the ordered list of persons accordingto the social network connection metrics (1702). In some embodiments, anamount of communication between a respective person and the requester onthe one or more communication applications is determined, and then aranking score for the respective person, is determined, wherein a factorin determining the ranking score for the respective person is thedetermined amount of communication between the respective person and therequester on the one or more communication applications (1704). Thecommunications applications may include social networking applications,social micro-blogs, email applications, and/or instant messagingapplications. For example, if a respective person has communicatedextensively with the requester by one or more communicationsapplications (e.g., extensive communications by email and social networkposts), then the requestor is likely to know the respective person quitewell and thus the facial image in the visual query is more likely to bethe respective person. In some embodiments, this factor is only usedwhen the amount of communication is above a pre-determined threshold(e.g., a set number of communications, a number of communications withina certain period of time, or a percentage of the total communications).In some embodiments, facial recognition search system 112-A determineswhether the amount of communication between the respective person andthe requester on the one or more communication applications exceeds athreshold, and a factor in determining the ranking score for therespective person is the determination of whether the amount ofcommunication between the respective person and the requester on the oneor more communication applications exceeds the threshold.

In some embodiments, a determination of whether the requester and arespective person are directly connected in a respective socialnetworking application is made, and then a ranking score for therespective person is determined, wherein a factor in determining theranking score for the respective person is the determination of whetherthe requester and the respective person are directly connected in arespective social networking application (1706). For example, if therequester and the respective person are directly connected as friends,then the requestor is likely to know the respective person quite welland thus the facial image in the visual query is more likely to be therespective person.

In cases where the person-specific data for the respective personincludes a plurality of characteristics, such as two or more of: name,address, occupation, group memberships, interests, age, hometown,personal statistics, and/or work information for the respective person,the same information is also retrieved for the requester, to the extentthat such information is available to facial recognition search system112-A. Then one or more personal similarity metrics are determined inaccordance with an extent to which the person- specific data of therequester is similar to the person-specific data of the respectiveidentified person. A ranking score for the respective identified personis determined, wherein one or more factors in determining the rankingscore for the respective identified person are the one or more personalsimilarity metrics (1708). For example, if the requester and therespective person are of similar age, similar occupation, and aremembers of similar groups, they are more likely to be friends and thusthe facial image in the visual query is more likely to be the respectiveperson.

In circumstances where the current location information for both therequester and the identified person are successfully obtained, a rankingscore for the respective identified person is determined, wherein afactor in determining the ranking score for the respective identifiedperson is whether current location information for the requester matchesthe current location information for the respective identified person(1710). For example, when both the requester and the respective personare determined to be at the same location, that proximity increases thelikelihood that the facial image in the visual query is the respectiveperson. And even more so, when the requester and the respective personare determined not to be at the same location, the lack of proximitygreatly decreases the likelihood that the facial image in the visualquery is the respective person. Furthermore, in some embodiments, ahistory or log of locations for both the requester and the identifiedperson are retrieved and compared with each other for a match. In someembodiments, the location logs of the requester and identified personare further compared with a location (and/or date and time)characteristic derived from the query image itself For example, if thequery location information indicates the image was taken July 2 in SantaCruz, Calif., and the logs of locations for both the requester and theidentified person also indicate that they were in Santa Cruz, Calif. onJuly 2, then this location match increases the likelihood that thefacial image in the visual query is that of the respective person.

In embodiments where the person-specific data for a respective personalso comprises characteristics derived from other images of therespective person (which was discussed with respect to step 1616), theranking is further in accordance with similarity between the receivedquery and the characteristics derived from other images of therespective person (1712). Various factors are used in determining theranking score for a respective person which are in accordance with thesecharacteristics derived from other images of the respective person(1714).

In some embodiments, the characteristics derived from other images ofthe respective person include image capture date (e.g., day of week, dayor month, and/or full date) and time information. Then one moresimilarity metrics is determined in accordance with an extent to whichthe received query has image capture date and time information similarto the date and time information of one or more other images of therespective person. A ranking score for the respective person isdetermined, wherein one or more factors in determining the ranking scorefor the respective person are the one or more similarity metrics (1716).In some embodiments, the similarity metric is a Boolean value (e.g.,yes/no or 1/0). In other embodiments, a similarity metric is a vector ofBoolean values (e.g., same date yes/no, within 1 hr yes/no, within 5 hrsyes/no, etc.). It can be a numeric value (e.g., between 0 and 1) thatmeasures the similarity. In some embodiments the similarity metric isdetermined for each other image of the respective person, but in someembodiments a group value for all of the images of the respective personis determined. In some embodiments, another characteristic derived fromthe images is place/location information, which can be used as anadditional or alternative similarity metric ad discussed above. Forexample, if the visual query has similar date, time, and/or locationinformation as one or more other images, that similarity increases thelikelihood that the facial image in the visual query is the respectiveperson who was in the one or more other images having similar date,time, and/or location information.

In some embodiments, the characteristics derived from other images ofthe respective person include occurrences information regarding anamount of occurrences of the respective person in images from the one ormore image sources. In some of these embodiments, a factor indetermining the ranking score for the respective person is theoccurrences information for the respective person (1718). For example,if numerous other images include the respective person, then therequestor is likely to know the respective person quite well, whichincreases the likelihood that the facial image in the visual query isthat of the respective person.

In some embodiments, the characteristics derived from other images ofthe respective person include visual factors including one or more of:an indoor habitat factor, an outdoor habitat factor, a gender factor, arace factor, a glasses factor, a facial hair factor, a head hair factor,a headwear factor, a clothing factor, and an eye color factor. In someof these embodiments, one or more factors in determining the rankingscore for the respective person include the visual factors for therespective person (1720).

In some situations, the visual query includes a plurality of facialimages. When more than one facial image is in the visual query, theninterconnections between them can be helpful in identifying themcorrectly. For example, if they have strong social connection metrics orappear in other images together, those facts increase the likelihoodthat they are together in the query image as well. In some embodiments,the visual query includes at least a respective facial image and asecond facial image. Images (herein called potential second imagematches) that potentially match the second facial image in accordancewith visual similarity criteria are identified. The potential secondimage matches are images from one or more image sources identified inaccordance with data regarding the requester. Then a second personassociated with the potential second image matches is identified. Forpurposes of this determination, it is assumed that the second person isidentified with a high degree of certainty. For each identified personas a potential match to the respective facial image, person-specificdata that includes second social connection metrics of socialconnectivity to the second person are obtained from the plurality ofapplications. Then, an ordered list of persons is generated by rankingthe one or more identified persons further in accordance with rankinginformation that includes at least the second social connection metrics.As such, a respective person's ranking is further in accordance withsecond social connection metrics comprising metrics of socialconnectivity to a second person in the query (1722). In other words, insome embodiments, both social connections to the requester and socialconnections to the second person are used in generating the ordered listof persons.

In other embodiments, one or more of the other factors discussed aboveare compared between the second person and each person identified as apotential match to find a best match. For example, if the second personand a respective person are employed at the same company, appear inother images that have similar date/time information, or communicationextensively with each other, then these factors can be used inidentifying them correctly. In another example, characteristics derivedfrom other images of the respective person include information regardingan amount of co-occurrences of the respective person and the secondperson in images from the one or more image sources; and when a rankingscore for the respective person is determined, a factor in determiningthe ranking score for the respective person is the amount ofco-occurrences of the person and the second person in images from theone or more image sources (1724).

FIG. 18A is a block diagram illustrating a portion of the data structureof a facial image database 114-A utilized by facial recognition searchsystem 112-A. In some embodiments, the facial image database containsone or more images of a person 1802 obtained from one or more imagessources identified in accordance with data regarding the requester. Insome embodiments, facial image database 114-A also contains a unique ID1804, or person identifier, for the person. Additional informationregarding the person is associated with the person identifier 1804 andis stored in a database of person-specific data 964. Some or all of theadditional information is then used in determining potential matches fora facial image in a visual query. For example, an ordered list ofidentified persons associated with potential image matches is generatedby ranking the persons in accordance with metrics of social connectivityto the requester, such as matching group memberships 1812 or strongsocial connections 1814. Data from the database of person specific data964 is used in addition to the potential image being visually similar tothe facial image in the visual query when determining an ordered list ofidentified persons. The database of person specific data 964 may includebut is not limited to any of the following items for the personidentified by the unique ID 1804: name 1806, address 1808, occupation1810, group memberships 1812, social network connections 1814 (explainedin more detail with regard to FIG. 18B), current location 1816, sharepreferences 1818, interests 1820, age 1822, hometown 1824, personalstatistics 1826, work information 1828. This information is obtainedfrom a plurality of applications such as communication applications,social networking applications, calendar applications, and collaborativeapplications. In some embodiments, the person specific data alsoincludes characteristics derived from one or more images of the person1830 as discussed with respect to FIG. 18C.

FIG. 18B illustrates an example of social network connections 1814. Insome embodiments, person-specific data for an identified person includessocial connections metrics of social connectivity to the requester(identified as the querier in FIG. 18B) which are obtained from aplurality of applications. The lines between the people in this figurerepresent one or more of their social connections to each other (such asa connection by email, instant message, and social networking website.)In some embodiments, the social distance between two people is used as afactor in determining a ranking score for the potential image matches.For example, if one potential matching image was an image of Person Cand another potential matching image was an image Person Y, in someembodiments, the potential matching image of Person C would receive ahigher social connectivity ranking factor (to be used in computing aranking score) than Person Y, because, ignoring all other factors, it ismore likely that requester was taking a picture of someone directlyconnected to the requester (Person C) than of someone three socialnetwork “hops” away (Person Y). Similarly, Person W would receive ahigher social connectivity ranking factor than Person A since Person Wis two social network “hops” away from requester, whereas Person A isthree social network “hops” away from requester. In some embodiments,the social network connections for a requester are also used todetermine which image sources to search in responding to the requester'svisual query. For example, in some embodiments, images in accountsbelonging to people with a direct social network connection are includedin the image sources searched for images matching a facial image in thevisual query, while images in accounts belonging to persons who do nothave a direct social network connection to the requester are notincluded in the image sources searched for images matching a facialimage in the visual query.

For some visual queries, other information from the database ofperson-specific data 964 of FIG. 18A is used in conjunction with thedistance or “hops” on a social network connections graph of FIG. 18B.For example, if the requester and the respective person live near oneanother, if they work in the same industry, are in the same socialnetwork “groups,” and if both have mobile devices that are currently atthe same location (as measured by, for example, GPS receivers in theirmobile devices), the ranking score of the respective person may still behigh even though that respective person is several “hops” away from therequester on a social network connections graph. In another example, ifthe respective person in a potential matching image is only one “hop”away from the requester on a social network connections graph, thatrespective person might be ranked high even despite a weak connectiondetermined through the database of person-specific data 964 (such asboth people being members of a large group membership, like sharing areligion or political party.)

In some embodiments, the requester can identify certain information fromthe database of person-specific data 964 as being more important thanother information from the database of person-specific data 964. Forexample, the requester might specify that information concerning theindustry in which a person works be given higher weight than otherperson-specific data, because the requester is attending a work-relatedfunction and thus query images are likely to include facial images ofother people working in the same industry as the requester. In otherexample, the requester might specify that information concerning age begiven higher weight than other person-specific data, because therequester is submitting query images from a party (or other function)attended by people who are all or primarily of the same age.

FIG. 18C is a block diagram illustrating some image derivedcharacteristics 1830, which are derived from images of each personassociated with the requester. In some embodiments, these derivedcharacteristics (derived from at least one image of the person) arestored by person identifier in a database. These derived characteristicsinclude one or more of (and typically two or more of): indoor habitatfactor 1832, an outdoor habitat factor 1834, a gender factor 1836, arace factor 1838, a glasses factor 1840, a facial hair factor 1842, ahead hair factor 1844, a headwear factor 1846, clothing factor 1847, aneye color factor 1848, as well as occurrences information regarding anamount of occurrences of the respective person in the one or more imagesources 1850, and information regarding an amount of co-occurrences ofthe respective person and with various additional people in images fromthe one or more image sources 1852. In some embodiments, the derivedcharacteristics also include metadata information from the images suchas date information 1854, time information 1856, and locationinformation 1858 for each image. Each derived characteristic 1830,derived from other images of a respective person, is given a value and aweight which is used in determining the ranking score for a respectiveperson when that derived characteristic is used.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A computer-implemented method of processing a visual query includinga facial image performed on a server system having one or moreprocessors and memory storing one or more programs for execution by theone or more processors to perform the method, comprising: receiving froma requester, a visual query comprising one or more facial imagesincluding a respective facial image; identifying potential image matchesthat potentially match the respective facial image in accordance withvisual similarity criteria, the potential image matches comprisingimages from one or more image sources identified in accordance with dataregarding the requester; identifying one or more persons associated withthe potential image matches; retrieving, for each identified person,person-specific data comprising social connection metrics of socialconnectivity to the requester obtained from a plurality of applicationsselected from the group consisting of communication applications, socialnetworking applications, calendar applications, and collaborativeapplications; generating an ordered list of persons by ranking the oneor more identified persons in accordance with one or more metrics ofvisual similarity between the respective facial image and the potentialimage matches and in accordance with ranking information comprising atleast the social connection metrics; and sending to the requester atleast one person identifier from the ordered list.
 2. Thecomputer-implemented method of claim 1, wherein the plurality ofapplications include one or more communication applications selectedfrom the group consisting of: social networking applications, socialmicro-blogs, email applications, and instant messaging applications;wherein the method further comprises: determining an amount ofcommunication between a respective person and the requester on the oneor more communication applications; and determining a ranking score forthe respective person, wherein a factor in determining the ranking scorefor the respective person is the determined amount of communicationbetween the respective person and the requester on the one or morecommunication applications.
 3. The computer-implemented method of claim2, including: determining whether the amount of communication betweenthe respective person and the requester on the one or more communicationapplications exceeds a threshold; wherein a factor in determining theranking score for the respective person is the determination of whetherthe amount of communication between the respective person and therequester on the one or more communication applications exceeds thethreshold.
 4. The computer implemented method of claim 1, furthercomprising: determining whether the requester and a respective personare directly connected in a respective social networking application,and determining a ranking score for the respective person, wherein afactor in determining the ranking score for the respective person is thedetermination of whether the requester and the respective person aredirectly connected in a respective social networking application.
 5. Thecomputer-implemented method of claim 1, wherein the person-specific datafor a respective person further comprises characteristics derived fromother images of the respective person; and wherein the ranking isfurther in accordance with similarity between the received query and thecharacteristics derived from other images of the respective person. 6.The computer-implemented method of claim 5, wherein the characteristicsderived from other images of the respective person include image capturedate and time information; and wherein the method further comprises:determining one more similarity metrics in accordance with an extent towhich the received query has image capture date and time informationsimilar to the date and time information of the other images of therespective person; and determining a ranking score for the respectiveperson, wherein one or more factors in determining the ranking score forthe respective person are the one or more similarity metrics.
 7. Thecomputer-implemented method of claim 5, wherein the characteristicsderived from other images of the respective person include occurrencesinformation regarding an amount of occurrences of the respective personin images from the one or more image sources; and wherein a factor indetermining the ranking score for the respective person is theoccurrences information for the respective person.
 8. Thecomputer-implemented method of claim 5, wherein the characteristicsderived from other images of the respective person comprises visualfactors including one or more of: an indoor habitat factor, an outdoorhabitat factor, a gender factor, a race factor, a glasses factor, afacial hair factor, a head hair factor, a headwear factor, clothingfactor, and an eye color factor; and wherein factors in determining theranking score for the respective person include the visual factors forthe respective person.
 9. The computer-implemented method of claim 1,wherein the visual query comprises a plurality of facial imagesincluding at least the respective facial image and a second facialimage; wherein the method further comprises: identifying potentialsecond image matches that potentially match the second facial image inaccordance with visual similarity criteria, the second potential imagematches comprising images from one or more image sources identified inaccordance with data regarding the requester; identifying a secondperson associated with the second facial image matches; retrieving, foreach identified person, person-specific data comprising second socialconnection metrics of social connectivity to the second person obtainedfrom the plurality of applications; and generating the ordered list ofpersons by ranking the one or more identified persons further inaccordance with ranking information comprising at least the secondsocial connection metrics.
 10. The computer-implemented method of claim9, wherein the person-specific data for a respective person furthercomprises characteristics derived from other images of the respectiveperson, the characteristics derived from other images of the respectiveperson including information regarding an amount of co-occurrences ofthe respective person and the second person in images from the one ormore image sources; and wherein the method further comprises:determining a ranking score for the respective person, wherein a factorin determining the ranking score for the respective person is the amountof co-occurrences of the person and the second person in images from theone or more image sources.
 11. The computer-implemented method of claim1, further comprising: prior to sending the requester at least oneperson identifier, checking an opt-in list and determining that theperson identifier is releasable to the requester.
 12. Thecomputer-implemented method of claim 1, wherein for a respectiveidentified person, the person-specific data further comprises one ormore of: name, address, occupation, group memberships, interests, age,hometown, personal statistics, and work information for the respectiveperson; and wherein the method further comprises: retrievingperson-specific data for the requester that also includes one or moreof: name, address, occupation, group memberships, interests, age,hometown, personal statistics, and work information for the respectiveperson; determining one or more personal similarity metrics inaccordance with an extent to which the person-specific data of therequester is similar to the person-specific data of the respectiveidentified person; and determining a ranking score for the respectiveidentified person, wherein one or more factors in determining theranking score for the respective identified person are the one or morepersonal similarity metrics.
 13. The computer-implemented method ofclaim 1, further comprising: obtaining current location information forthe requester; obtaining current location information for a respectiveidentified person; and determining a ranking score for the respectiveidentified person, wherein a factor in determining the ranking score forthe respective identified person is whether current location informationfor the requester matches the current location information for therespective identified person.
 14. The computer-implemented method ofclaim 1, further comprising: after the sending, receiving from therequester a selection of a person identifier; and storing the respectivefacial image of the visual query as an additional image of a respectiveperson corresponding to the selected person identifier.
 15. Thecomputer-implemented method of claim 1, wherein the visual querycomprises a plurality of facial images including at least the respectivefacial image and a second facial image; and wherein the method furthercomprises: prior to identifying potential image matches, receiving aselection of the respective facial image from the requester.
 16. Aserver system, for processing a visual query including a facial image,comprising: one or more processors for executing programs; memorystoring one or more programs be executed by the one or more processors;the one or more programs comprising instructions for: receiving from arequester, a visual query comprising one or more facial images includinga respective facial image; identifying potential image matches thatpotentially match the respective facial image in accordance with visualsimilarity criteria, the potential image matches comprising images fromone or more image sources identified in accordance with data regardingthe requester; identifying one or more persons associated with thepotential image matches; retrieving, for each identified person,person-specific data comprising social connection metrics of socialconnectivity to the requester obtained from a plurality of applicationsselected from the group consisting of communication applications, socialnetworking applications, calendar applications, and collaborativeapplications; generating an ordered list of persons by ranking the oneor more identified persons in accordance with one more metrics of visualsimilarity between the respective facial image and the potential imagematches and in accordance with ranking information comprising at leastthe social connection metrics; and sending to the requester at least oneperson identifier from the ordered list.
 17. The server system of claim16, wherein the plurality of applications include one or morecommunication applications selected from the group consisting of: socialnetworking applications, social micro-blogs, email applications, andinstant messaging applications; wherein the method further comprises:determining an amount of communication between a respective person andthe requester on the one or more communication applications; anddetermining a ranking score for the respective person, wherein a factorin determining the ranking score for the respective person is thedetermined amount of communication between the respective person and therequester on the one or more communication applications.
 18. Anon-transitory computer readable storage medium storing one or moreprograms configured for execution by a computer, the one or moreprograms comprising instructions for: receiving from a requester, avisual query comprising one or more facial images including a respectivefacial image; identifying potential image matches that potentially matchthe respective facial image in accordance with visual similaritycriteria, the potential image matches comprising images from one or moreimage sources identified in accordance with data regarding therequester; identifying one or more persons associated with the potentialimage matches; retrieving, for each identified person, person-specificdata comprising social connection metrics of social connectivity to therequester obtained from a plurality of applications selected from thegroup consisting of communication applications, social networkingapplications, calendar applications, and collaborative applications;generating an ordered list of persons by ranking the one or moreidentified persons in accordance with one more metrics of visualsimilarity between the respective facial image and the potential imagematches and in accordance with ranking information comprising at leastthe social connection metrics; and sending to the requester at least oneperson identifier from the ordered list.
 19. The computer readablestorage medium of claim 18, wherein the plurality of applicationsinclude one or more communication applications selected from the groupconsisting of: social networking applications, social micro-blogs, emailapplications, and instant messaging applications; wherein the methodfurther comprises: determining an amount of communication between arespective person and the requester on the one or more communicationapplications; and determining a ranking score for the respective person,wherein a factor in determining the ranking score for the respectiveperson is the determined amount of communication between the respectiveperson and the requester on the one or more communication applications.